OpenRaiser/Intern-Atlas huggingface dataset:
License Plate Images Generation with Diffusion Models
Venue: arXiv cs.CV 2025
Year: 2025

Despite the evident practical importance of license plate recognition (LPR), corresponding research is limited by the volume of publicly available datasets due to privacy regulations such as the General Data Protection Regulation (GDPR). To address this challenge, synthetic data generation has emerged as a promising approach. In this paper, we propose to synthesize realistic license plates (LPs) using diffusion models, inspired by recent advances in image and video generation. In our experiments a diffusion model was successfully trained on a Ukrainian LP dataset, and 1000 synthetic images were generated for detailed analysis. Through manual classification and annotation of the generated images, we performed a thorough study of the model output, such as success rate, character distributions, and type of failures. Our contributions include experimental validation of the efficacy of diffusion models for LP synthesis, along with insights into the characteristics of the generated data. Furthermore, we have prepared a synthetic dataset consisting of 10,000 LP images, publicly available at https://zenodo.org/doi/10.5281/zenodo.13342102. Conducted experiments empirically confirm the usefulness of synthetic data for the LPR task. Despite the initial performance gap between the model trained with real and synthetic data, the expansion of the training data set with pseudolabeled synthetic data leads to an improvement in LPR accuracy by 3% compared to baseline.
DoubleDiffusion: Combining Heat Diffusion with Denoising Diffusion for Texture Generation on 3D Meshes
Venue: arXiv cs.CV 2025
Year: 2025

This paper addresses the problem of generating textures for 3D mesh assets. Existing approaches often rely on image diffusion models to generate multi-view image observations, which are then transformed onto the mesh surface to produce a single texture. However, due to the gap between multi-view images and 3D space, such process is susceptible to arange of issues such as geometric inconsistencies, visibility occlusion, and baking artifacts. To overcome this problem, we propose a novel approach that directly generates texture on 3D meshes. Our approach leverages heat dissipation diffusion, which serves as an efficient operator that propagates features on the geometric surface of a mesh, while remaining insensitive to the specific layout of the wireframe. By integrating this technique into a generative diffusion pipeline, we significantly improve the efficiency of texture generation compared to existing texture generation methods. We term our approach DoubleDiffusion, as it combines heat dissipation diffusion with denoising diffusion to enable native generative learning on 3D mesh surfaces.
SALT: Sales Autocompletion Linked Business Tables Dataset
Venue: arXiv cs.LG 2025
Year: 2025

Foundation models, particularly those that incorporate Transformer architectures, have demonstrated exceptional performance in domains such as natural language processing and image processing. Adapting these models to structured data, like tables, however, introduces significant challenges. These difficulties are even more pronounced when addressing multi-table data linked via foreign key, which is prevalent in the enterprise realm and crucial for empowering business use cases. Despite its substantial impact, research focusing on such linked business tables within enterprise settings remains a significantly important yet underexplored domain. To address this, we introduce a curated dataset sourced from an Enterprise Resource Planning (ERP) system, featuring extensive linked tables. This dataset is specifically designed to support research endeavors in table representation learning. By providing access to authentic enterprise data, our goal is to potentially enhance the effectiveness and applicability of models for real-world business contexts.
Designing Telepresence Robots to Support Place Attachment
Venue: arXiv cs.HC 2025
Year: 2025

People feel attached to places that are meaningful to them, which psychological research calls "place attachment." Place attachment is associated with self-identity, self-continuity, and psychological well-being. Even small cues, including videos, images, sounds, and scents, can facilitate feelings of connection and belonging to a place. Telepresence robots that allow people to see, hear, and interact with a remote place have the potential to establish and maintain a connection with places and support place attachment. In this paper, we explore the design space of robotic telepresence to promote place attachment, including how users might be guided in a remote place and whether they experience the environment individually or with others. We prototyped a telepresence robot that allows one or more remote users to visit a place and be guided by a local human guide or a conversational agent. Participants were 38 university alumni who visited their alma mater via the telepresence robot. Our findings uncovered four distinct user personas in the remote experience and highlighted the need for social participation to enhance place attachment. We generated design implications for future telepresence robot design to support people's connections with places of personal significance.
Feasibility of short blocklength Reed-Muller codes for physical layer security in real environment
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we investigate the application of Reed-Muller (RM) codes for Physical-layer security in a real world wiretap channel scenario. Utilizing software-defined radios (SDRs) in a real indoor environment, we implement a coset coding scheme that leverages the hierarchical structure of RM codes to secure data transmission. The generator matrix of the RM code is used to partition codewords into cosets in the usual way, where each message corresponds to a unique coset, and auxiliary bits select specific codewords within each coset. This approach enables the legitimate receiver (Bob) can decode the transmitted message with minimal information leakage to eavesdropper (Eve) thus protecting the confidentiality of the communication with the help of coset structure. Mutual information neural estimation (MINE) is used to quantify information leakage and validate the effectiveness of the scheme. Experimental results indicate that RM codes can achieve robust security even in practical environments affected by real-world channel impairments. These findings demonstrate the potential of RM codes as an efficient solution for physical-layer security, particularly for applications that require low latency and short blocklengths.
Anomaly Triplet-Net: Progress Recognition Model Using Deep Metric Learning Considering Occlusion for Manual Assembly Work
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, a progress recognition method consider occlusion using deep metric learning is proposed to visualize the product assembly process in a factory. First, the target assembly product is detected from images acquired from a fixed-point camera installed in the factory using a deep learning-based object detection method. Next, the detection area is cropped from the image. Finally, by using a classification method based on deep metric learning on the cropped image, the progress of the product assembly work is estimated as a rough progress step. As a specific progress estimation model, we propose an Anomaly Triplet-Net that adds anomaly samples to Triplet Loss for progress estimation considering occlusion. In experiments, an 82.9% success rate is achieved for the progress estimation method using Anomaly Triplet-Net. We also experimented with the practicality of the sequence of detection, cropping, and progression estimation, and confirmed the effectiveness of the overall system.
Unwinding NFTs in the Shadow of IP Law
Venue: arXiv cs.CY 2025
Year: 2025

Amid the surge of intellectual property (IP) disputes surrounding non-fungible tokens (NFTs), some scholars have advocated for the application of personal property or sales law to regulate NFT minting and transactions, contending that IP laws unduly hinder the development of the NFT market. This Article counters these proposals and argues that the existing IP system stands as the most suitable regulatory framework for governing the evolving NFT market. Compared to personal property or sales law, IP laws can more effectively address challenges such as tragedies of the commons and anticommons in the NFT market. NFT communities have also developed their own norms and licensing agreements upon existing IP laws to regulate shared resources. Moreover, the IP regimes, with both static and dynamic institutional designs, can effectively balance various policy concerns, such as innovation, fair competition, and consumer protection, which alternative proposals struggle to provide.
Applying Large Language Models in Knowledge Graph-based Enterprise Modeling: Challenges and Opportunities
Venue: arXiv cs.MA 2025
Year: 2025

The role of large language models (LLMs) in enterprise modeling has recently started to shift from academic research to that of industrial applications. Thereby, LLMs represent a further building block for the machine-supported generation of enterprise models. In this paper we employ a knowledge graph-based approach for enterprise modeling and investigate the potential benefits of LLMs in this context. In addition, the findings of an expert survey and ChatGPT-4o-based experiments demonstrate that LLM-based model generations exhibit minimal variability, yet remain constrained to specific tasks, with reliability declining for more intricate tasks. The survey results further suggest that the supervision and intervention of human modeling experts are essential to ensure the accuracy and integrity of the generated models.
What Does a Software Engineer Look Like? Exploring Societal Stereotypes in LLMs
Venue: arXiv cs.SE 2025
Year: 2025

Large language models (LLMs) have rapidly gained popularity and are being embedded into professional applications due to their capabilities in generating human-like content. However, unquestioned reliance on their outputs and recommendations can be problematic as LLMs can reinforce societal biases and stereotypes. This study investigates how LLMs, specifically OpenAI's GPT-4 and Microsoft Copilot, can reinforce gender and racial stereotypes within the software engineering (SE) profession through both textual and graphical outputs. We used each LLM to generate 300 profiles, consisting of 100 gender-based and 50 gender-neutral profiles, for a recruitment scenario in SE roles. Recommendations were generated for each profile and evaluated against the job requirements for four distinct SE positions. Each LLM was asked to select the top 5 candidates and subsequently the best candidate for each role. Each LLM was also asked to generate images for the top 5 candidates, providing a dataset for analysing potential biases in both text-based selections and visual representations. Our analysis reveals that both models preferred male and Caucasian profiles, particularly for senior roles, and favoured images featuring traits such as lighter skin tones, slimmer body types, and younger appearances. These findings highlight underlying societal biases influence the outputs of LLMs, contributing to narrow, exclusionary stereotypes that can further limit diversity and perpetuate inequities in the SE field. As LLMs are increasingly adopted within SE research and professional practices, awareness of these biases is crucial to prevent the reinforcement of discriminatory norms and to ensure that AI tools are leveraged to promote an inclusive and equitable engineering culture rather than hinder it.
Deep Learning-based Compression Detection for explainable Face Image Quality Assessment
Venue: arXiv cs.CV 2025
Year: 2025

The assessment of face image quality is crucial to ensure reliable face recognition. In order to provide data subjects and operators with explainable and actionable feedback regarding captured face images, relevant quality components have to be measured. Quality components that are known to negatively impact the utility of face images include JPEG and JPEG 2000 compression artefacts, among others. Compression can result in a loss of important image details which may impair the recognition performance. In this work, deep neural networks are trained to detect the compression artefacts in a face images. For this purpose, artefact-free facial images are compressed with the JPEG and JPEG 2000 compression algorithms. Subsequently, the PSNR and SSIM metrics are employed to obtain training labels based on which neural networks are trained using a single network to detect JPEG and JPEG 2000 artefacts, respectively. The evaluation of the proposed method shows promising results: in terms of detection accuracy, error rates of 2-3% are obtained for utilizing PSNR labels during training. In addition, we show that error rates of different open-source and commercial face recognition systems can be significantly reduced by discarding face images exhibiting severe compression artefacts. To minimize resource consumption, EfficientNetV2 serves as basis for the presented algorithm, which is available as part of the OFIQ software.
MHGNet: Multi-Heterogeneous Graph Neural Network for Traffic Prediction
Venue: arXiv cs.LG 2025
Year: 2025

In recent years, traffic flow prediction has played a crucial role in the management of intelligent transportation systems. However, traditional forecasting methods often model non-Euclidean low-dimensional traffic data as a simple graph with single-type nodes and edges, failing to capture similar trends among nodes of the same type. To address this limitation, this paper proposes MHGNet, a novel framework for modeling spatiotemporal multi-heterogeneous graphs. Within this framework, the STD Module decouples single-pattern traffic data into multi-pattern traffic data through feature mappings of timestamp embedding matrices and node embedding matrices. Subsequently, the Node Clusterer leverages the Euclidean distance between nodes and different types of limit points to perform clustering with O(N) time complexity. The nodes within each cluster undergo residual subgraph convolution within the spatiotemporal fusion subgraphs generated by the DSTGG Module, followed by processing in the SIE Module for node repositioning and redistribution of weights. To validate the effectiveness of MHGNet, this paper conducts extensive ablation studies and quantitative evaluations on four widely used benchmarks, demonstrating its superior performance.
Advancing the Understanding of Fine-Grained 3D Forest Structures using Digital Cousins and Simulation-to-Reality: Methods and Datasets
Venue: arXiv cs.CV 2025
Year: 2025

Understanding and analyzing the spatial semantics and structure of forests is essential for accurate forest resource monitoring and ecosystem research. However, the lack of large-scale and annotated datasets has limited the widespread use of advanced intelligent techniques in this field. To address this challenge, a fully automated synthetic data generation and processing framework based on the concepts of Digital Cousins and Simulation-to-Reality (Sim2Real) is proposed, offering versatility and scalability to any size and platform. Using this process, we created the Boreal3D, the world's largest forest point cloud dataset. It includes 1000 highly realistic and structurally diverse forest plots across four different platforms, totaling 48,403 trees and over 35.3 billion points. Each point is labeled with semantic, instance, and viewpoint information, while each tree is described with structural parameters such as diameter, crown width, leaf area, and total volume. We designed and conducted extensive experiments to evaluate the potential of Boreal3D in advancing fine-grained 3D forest structure analysis in real-world applications. The results demonstrate that with certain strategies, models pre-trained on synthetic data can significantly improve performance when applied to real forest datasets. Especially, the findings reveal that fine-tuning with only 20% of real-world data enables the model to achieve performance comparable to models trained exclusively on entire real-world data, highlighting the value and potential of our proposed framework. The Boreal3D dataset, and more broadly, the synthetic data augmentation framework, is poised to become a critical resource for advancing research in large-scale 3D forest scene understanding and structural parameter estimation.
SMIR: Efficient Synthetic Data Pipeline To Improve Multi-Image Reasoning
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language Models (VLMs) excel at understanding single images, aided by high-quality instruction datasets. However, multi-image reasoning remains underexplored in the open-source community due to two key challenges: (1) scaling datasets with correlated images and complex reasoning instructions is resource-intensive, and (2) robust evaluation benchmarks for multi-image tasks are lacking. To address this, we introduce SMiR, a synthetic data-generation pipeline for multi-image reasoning, along with a high-quality dataset generated using this pipeline. SMiR efficiently extracts correlated images via multimodal embeddings, integrates visual and descriptive information, and leverages open-source LLMs to generate quality instructions. Using this approach, we produce 160K synthetic training samples, offering a cost-effective alternative to closed-source solutions. Additionally, we present SMiR-Bench, a multi-image reasoning benchmark comprising 200 diverse examples across seven complex reasoning tasks. SMiR-Bench is multi-turn and employs a VLM judge to evaluate free-form responses, providing a comprehensive assessment of model expressiveness and reasoning capability across modalities. We demonstrate the effectiveness of SMiR by fine-tuning open-source VLMs and evaluating them on SMiR-Bench.
AutoFish: Dataset and Benchmark for Fine-grained Analysis of Fish
Venue: arXiv cs.CV 2025
Year: 2025

Automated fish documentation processes are in the near future expected to play an essential role in sustainable fisheries management and for addressing challenges of overfishing. In this paper, we present a novel and publicly available dataset named AutoFish designed for fine-grained fish analysis. The dataset comprises 1,500 images of 454 specimens of visually similar fish placed in various constellations on a white conveyor belt and annotated with instance segmentation masks, IDs, and length measurements. The data was collected in a controlled environment using an RGB camera. The annotation procedure involved manual point annotations, initial segmentation masks proposed by the Segment Anything Model (SAM), and subsequent manual correction of the masks. We establish baseline instance segmentation results using two variations of the Mask2Former architecture, with the best performing model reaching an mAP of 89.15%. Additionally, we present two baseline length estimation methods, the best performing being a custom MobileNetV2-based regression model reaching an MAE of 0.62cm in images with no occlusion and 1.38cm in images with occlusion. Link to project page: https://vap.aau.dk/autofish/.
Multi-label Cross-lingual automatic music genre classification from lyrics with Sentence BERT
Venue: arXiv cs.IR 2025
Year: 2025

Music genres are shaped by both the stylistic features of songs and the cultural preferences of artists' audiences. Automatic classification of music genres using lyrics can be useful in several applications such as recommendation systems, playlist creation, and library organization. We present a multi-label, cross-lingual genre classification system based on multilingual sentence embeddings generated by sBERT. Using a bilingual Portuguese-English dataset with eight overlapping genres, we demonstrate the system's ability to train on lyrics in one language and predict genres in another. Our approach outperforms the baseline approach of translating lyrics and using a bag-of-words representation, improving the genrewise average F1-Score from 0.35 to 0.69. The classifier uses a one-vs-all architecture, enabling it to assign multiple genre labels to a single lyric. Experimental results reveal that dataset centralization notably improves cross-lingual performance. This approach offers a scalable solution for genre classification across underrepresented languages and cultural domains, advancing the capabilities of music information retrieval systems.
Detection of metadata manipulations: Finding sneaked references in the scholarly literature
Venue: arXiv cs.DL 2025
Year: 2025

We report evidence of a new set of sneaked references discovered in the scientific literature. Sneaked references are references registered in the metadata of publications without being listed in reference section or in the full text of the actual publications where they ought to be found. We document here 80,205 references sneaked in metadata of the International Journal of Innovative Science and Research Technology (IJISRT). These sneaked references are registered with Crossref and all cite -- thus benefit -- this same journal. Using this dataset, we evaluate three different methods to automatically identify sneaked references. These methods compare reference lists registered with Crossref against the full text or the reference lists extracted from PDF files. In addition, we report attempts to scale the search for sneaked references to the scholarly literature.
Strip R-CNN: Large Strip Convolution for Remote Sensing Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

While witnessed with rapid development, remote sensing object detection remains challenging for detecting high aspect ratio objects. This paper shows that large strip convolutions are good feature representation learners for remote sensing object detection and can detect objects of various aspect ratios well. Based on large strip convolutions, we build a new network architecture called Strip R-CNN, which is simple, efficient, and powerful. Unlike recent remote sensing object detectors that leverage large-kernel convolutions with square shapes, our Strip R-CNN takes advantage of sequential orthogonal large strip convolutions in our backbone network StripNet to capture spatial information. In addition, we improve the localization capability of remote-sensing object detectors by decoupling the detection heads and equipping the localization branch with strip convolutions in our strip head. Extensive experiments on several benchmarks, for example DOTA, FAIR1M, HRSC2016, and DIOR, show that our Strip R-CNN can greatly improve previous work. In particular, our 30M model achieves 82.75% mAP on DOTA-v1.0, setting a new state-of-the-art record. Our code will be made publicly available.Code is available at https://github.com/YXB-NKU/Strip-R-CNN.